{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pima Indians Diabetes 病情分类\n",
    "根据Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集）进行分类器练\n",
    "习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#评价指标logloss\n",
    "from sklearn.metrics import log_loss\n",
    "#SVM并不能直接输出各类的概率，所以采用正确率作为模型预测性能的度量\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据读取&数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据\n",
    "# path to where the data lies\n",
    "dpath = './diabetes.csv'\n",
    "data = pd.read_csv(dpath)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据简要信息，已知该数据集的缺失值已采用零值编码\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#各属性的统计特性\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "已经空值数据已经采用零值编码筛选出空值，但部分特征不该出现零值，比如Glucose、BloodPressure、SkinThickness、Insulin、BMI，需要对这部分零值数据进行重新编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose            5\n",
      "BloodPressure     35\n",
      "SkinThickness    227\n",
      "Insulin          374\n",
      "BMI               11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_cols = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "print((data[NaN_cols] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Glucose、BloodPressure、BMI0值较少，但是SkinThickness、Insulin中0值比例较大。\n",
    "为了保证有足够的数据进行训练模型，需要针对不同的列有不同的缺失值判断策略。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8b6980e48>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target 分布，看看各类样本分布是否均衡\n",
    "sns.countplot(data.Outcome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "两种分类的样本分布不均衡。交叉验证对分类任务缺省的是采用StratifiedKFold，在每折采样时根据各类样本按比例采样。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入属性的直方图／分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pregnancies(怀孕次数)\n",
    "fig = plt.figure()\n",
    "sns.countplot(data.Pregnancies)\n",
    "plt.xlabel('Number of Pregnancies')\n",
    "plt.ylabel('Number of occurrences')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有出现14、15、17等少数比较少见的值，一般情况下可能会被当做异常值。有些疾病的症状对应的数据本身就少见，异常值可能意味着生病，所以不能删除(下面同理)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8b69bb8d0>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Pregnancies', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目测怀孕次数越多的人群中，发病比例越大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Develop\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Glucose(血浆葡萄糖浓度)\n",
    "fig = plt.figure()\n",
    "sns.distplot(data['Glucose'], bins=30, kde=True)\n",
    "plt.xlabel('Glucose')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"Outcome\", y=\"Glucose\" ,data=data, hue='Outcome')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Develop\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BloodPressure(舒张压（mm Hg）)\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.BloodPressure, kde='True')\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('Number of occurrence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='BloodPressure', hue='Outcome',data=data)\n",
    "plt.xlabel('Outcome', fontsize=12)\n",
    "plt.ylabel('BloodPressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来发病人群的血压较高些，但不像怀孕次数与发病例的关联性那么明显"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SkinThickness(三头肌皮肤褶层厚度（mm）)\n",
    "plt.scatter(range(data.shape[0]), data['SkinThickness'])\n",
    "plt.title('Distribution of SkinThickness')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Develop\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8b7dde128>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['SkinThickness'], bins=30, kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺失了较多的样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Develop\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8b7bcb1d0>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Insulin(血清胰岛素含量（μU/ ml）)\n",
    "sns.distplot(data['Insulin'], bins=30, kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺失了较多的样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8b7ef84e0>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Insulin', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Develop\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8b7f87748>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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38/DRf//m4DNyHrC7+4z8HXDaHGQ+Bfg28ItDYyec2StoJakB8zyNI0kakWUvSQ2w7CWpAZa9JDXAspekBlj2alqSZ7rVBL+c5P4kr+7GNyWpJO8Z2nddkh8n+etu+0+T/OG0sksnwrJX635Yg1UFz2WwmN6fDz32KPCGoe1rGJxTLs0dy176qRcxWE74qB8Ce5McXb73t4A7Vj2VNAEr8h200hx5QbdK5vMZLBV72TGP3wZcm+SbwDMM1qh5yepGlPqz7NW6H9ZglUySXAJ8PMmrhh7/HPAe4Eng9inkkybCaRypU1X/AqwDFobGfgTsAd7BYBVQaS55ZC91krwcOInBolOnDD30l8A/VNW3BwuCSvPHslfrjs7Zw+BLQ7ZU1TPDpV5VD+NZOJpzrnopSQ1wzl6SGmDZS1IDLHtJaoBlL0kNsOwlqQGWvSQ1wLKXpAZY9pLUgP8HhfpJzH4WJzwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BMI(体重指数（体重，kg /（身高，m）^ 2）)\n",
    "sns.distplot(data['BMI'], bins=30, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8b7ffab00>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='BMI', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目测较高BMI的样本中，发病比例较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Develop\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrence')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DiabetesPedigreeFunction(糖尿病家族史)\n",
    "sns.distplot(data.DiabetesPedigreeFunction, kde=False)\n",
    "plt.xlabel('DiabetesPedigreeFunction', fontsize=12)\n",
    "plt.ylabel('Number of occurrence', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8b7d81668>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.countplot(x='DiabetesPedigreeFunction', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "两者间的关系不明显"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Develop\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8acd30940>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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50bhrkUbBoJe6A3S+QHcQmNQcg15Htf5cSufTnS72ir7tGUk+0J9L/PYkdyS5rL/v5Un+tT8R2mfnDs2XJplBr6PdpXTncf8P4JEkZwG/C6wDfo3uaMbz4BfnXvoH4LKqejlwHXDNOIqWlmJkPw4uTYnNdCdmg+7Q9M3AM4FPVdXPge8luau//3TgTODO7vQlHEN3Clppohn0Omr151C5EDgzSdEFd/HEOXCe8hDg/qo67wiVKA2FUzc6ml0GfKyqXlBV66pqLd0vNT0M/F4/V38ycEHffw8wk+QXUzlJXjKOwqWlMOh1NNvMU7feb6L7oYhZutMWf5DujKmPVtXP6P44vCfJV4F76c4xLk00z14pLSDJc6rqx/30zt10v0b1vXHXJS2Hc/TSwm7vfyzmWcBfG/KaZm7RS1LjnKOXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjfs/BtPpG+2qagEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age(年龄)\n",
    "sns.distplot(data.Age, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8bd563c18>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.countplot(x='Age', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本中，年龄大的人群发病比例比较大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                   0\n",
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "Outcome                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data[NaN_cols] = data[NaN_cols].replace(0, np.NaN)\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data_medians = data.median()\n",
    "data = data.fillna(data_medians)\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.51659096  0.76034612  0.62370402 ...  0.83629581  0.52952571\n",
      "   0.56793202]\n",
      " [ 1.81201827  0.23531342 -0.82718565 ...  1.32052242 -0.06968859\n",
      "   0.39844951]\n",
      " [ 0.92573636 -0.65067927  0.14007413 ...  0.73358108 -0.79424873\n",
      "   0.99163829]\n",
      " ...\n",
      " [ 2.69830017  0.13686978  1.42975384 ...  1.64334015  0.36443605\n",
      "   0.73741453]\n",
      " [ 0.03945446  1.61352426 -0.02113583 ...  1.67268722  0.04648561\n",
      "  -0.61844554]\n",
      " [ 1.51659096 -0.65067927  0.30128409 ...  0.10261914  0.61512775\n",
      "   1.07637954]]\n"
     ]
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "y = data['Outcome'].values\n",
    "X = data.drop('Outcome', axis =1)\n",
    "# 随机采样20%的样本作为测试数据，其余作为训练数据\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss_X = StandardScaler()\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "print(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 1000]}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "penaltys = ['l1', 'l2']\n",
    "Cs = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 1000]\n",
    "# params = dict(penalty = penaltys, C = Cs)\n",
    "params = {'penalty':penaltys, 'C':Cs}\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用neg_log_loss为评价指标\n",
    "lr_genalty = LogisticRegression()\n",
    "grid = GridSearchCV(lr_genalty, params, cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4910183399173995\n",
      "{'C': 0.1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# 得到最佳得分和最佳参数\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用正确率为评价指标\n",
    "lr_genalty = LogisticRegression()\n",
    "grid = GridSearchCV(lr_genalty, params, cv=5)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.7654723127035831\n",
      "{'C': 0.1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "# 得到最佳得分和最佳参数\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 线性SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 1000]}\n",
      "0.7703583061889251\n",
      "{'C': 0.01}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "Cs = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 1000]\n",
    "params_svm = {'C': Cs}\n",
    "print(params_svm)\n",
    "\n",
    "grid = GridSearchCV(SVC(kernel='linear'), params_svm, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RBF核SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7687296416938111\n",
      "{'C': 100, 'gamma': 0.0001}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "Cs = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 1000]\n",
    "gammas = [0.000001, 0.00001 ,0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 1000]\n",
    "params_rbf = {'C': Cs, 'gamma':gammas}\n",
    "grid = GridSearchCV(SVC(kernel='rbf'), params_rbf, cv=5)\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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